Transcript cern_stat_3

Introduction to Statistics − Day 3
Lecture 1
Probability
Random variables, probability densities, etc.
Lecture 2
Brief catalogue of probability densities
The Monte Carlo method.
→
Lecture 3
Statistical tests
Fisher discriminants, neural networks, etc
Goodness-of-fit tests
Lecture 4
Parameter estimation
Maximum likelihood and least squares
Interval estimation (setting limits)
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Statistical tests (in a particle physics context)
Suppose the result of a measurement for an individual event
is a collection of numbers
x1 = number of muons,
x2 = mean pt of jets,
x3 = missing energy, ...
follows some n-dimensional joint pdf, which depends on
the type of event produced, i.e., was it
For each reaction we consider we will have a hypothesis for the
pdf of , e.g.,
etc.
E.g. call H0 the signal hypothesis (the event type we want);
H1, H2, ... are background hypotheses.
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Selecting events
Suppose we have a data sample with two kinds of events,
corresponding to hypotheses H0 and H1 and we want to select
those of type H0.
Each event is a point in space. What ‘decision boundary’
should we use to accept/reject events as belonging to event
type H0?
H1
Perhaps select events
with ‘cuts’:
H0
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Other ways to select events
Or maybe use some other sort of decision boundary:
linear
or nonlinear
H1
H1
H0
H0
accept
accept
How can we do this in an ‘optimal’ way?
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Test statistics
Construct a ‘test statistic’ of lower dimension (e.g. scalar)
Try to compactify data without losing ability to discriminate
between hypotheses.
We can work out the pdfs
Decision boundary is now a
single ‘cut’ on t.
This effectively divides the
sample space into two regions,
where we accept or reject H0.
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Significance level and power of a test
Probability to reject H0 if it is true
(error of the 1st kind):
(significance level)
Probability to accept H0 if H1 is true
(error of the 2nd kind):
(1 - b = power)
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Efficiency of event selection
Probability to accept an event which
is signal (signal efficiency):
Probability to accept an event which
is background (background efficiency):
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Purity of event selection
Suppose only one background type b; overall fractions of signal
and background events are ps and pb (prior probabilities).
Suppose we select events with t < tcut. What is the
‘purity’ of our selected sample?
Here purity means the probability to be signal given that
the event was accepted. Using Bayes’ theorem we find:
So the purity depends on the prior probabilities as well as on the
signal and background efficiencies.
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Constructing a test statistic
How can we select events in an ‘optimal way’?
Neyman-Pearson lemma (proof in Brandt Ch. 8) states:
To get the lowest eb for a given es (highest power for a given
significance level), choose acceptance region such that
where c is a constant which determines es.
Equivalently, optimal scalar test statistic is
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Why Neyman-Pearson doesn’t always help
The problem is that we usually don’t have explicit formulae for
the pdfs
Instead we may have Monte Carlo models for signal and
background processes, so we can produce simulated data,
and enter each event into an n-dimensional histogram.
Use e.g. M bins for each of the n dimensions, total of Mn cells.
But n is potentially large, → prohibitively large number of cells
to populate with Monte Carlo data.
Compromise: make Ansatz for form of test statistic
with fewer parameters; determine them (e.g. using MC) to
give best discrimination between signal and background.
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Linear test statistic
Ansatz:
Choose the parameters a1, ..., an so that the pdfs
have maximum ‘separation’. We want:
g (t)
large distance between
mean values, small widths
ms
ss
mb
sb
t
→ Fisher: maximize
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Fisher discriminant
Using this definition of separation gives a Fisher discriminant.
H1
Corresponds to a linear
decision boundary.
H0
accept
Equivalent to Neyman-Pearson if the signal and background
pdfs are multivariate Gaussian with equal covariances;
otherwise not optimal, but still often a simple, practical solution.
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Nonlinear test statistics
The optimal decision boundary may not be a hyperplane,
→ nonlinear test statistic
Multivariate statistical methods
are a Big Industry:
Neural Networks,
Support Vector Machines,
Kernel density methods,
Decision trees, ...
H1
H0
accept
New software for HEP, e.g.,
TMVA , Höcker, Stelzer, Tegenfeldt, Voss, Voss, physics/0703039
StatPatternRecognition, I. Narsky, physics/0507143
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Neural network example from LEP II
Signal: e+e- → W+W-
(often 4 well separated hadron jets)
Background: e+e- → qqgg (4 less well separated hadron jets)
← input variables based on jet
structure, event shape, ...
none by itself gives much separation.
Neural network output does better...
(Garrido, Juste and Martinez, ALEPH 96-144)
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Testing goodness-of-fit
Suppose hypothesis H predicts pdf
for a set of
observations
We observe a single point in this space:
What can we say about the validity of H in light of the data?
Decide what part of the
data space represents less
compatibility with H than
does the point
(Not unique!)
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compatible
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compatible
with H
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p-values
Express ‘goodness-of-fit’ by giving the p-value for H:
p = probability, under assumption of H, to observe data with
equal or lesser compatibility with H relative to the data we got.
This is not the probability that H is true!
In frequentist statistics we don’t talk about P(H) (unless H
represents a repeatable observation). In Bayesian statistics we do;
use Bayes’ theorem to obtain
where p (H) is the prior probability for H.
For now stick with the frequentist approach;
result is p-value, regrettably easy to misinterpret as P(H).
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p-value example: testing whether a coin is ‘fair’
Probability to observe n heads in N coin tosses is binomial:
Hypothesis H: the coin is fair (p = 0.5).
Suppose we toss the coin N = 20 times and get n = 17 heads.
Region of data space with equal or lesser compatibility with
H relative to n = 17 is: n = 17, 18, 19, 20, 0, 1, 2, 3. Adding
up the probabilities for these values gives:
i.e. p = 0.0026 is the probability of obtaining such a bizarre
result (or more so) ‘by chance’, under the assumption of H.
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The significance of an observed signal
Suppose we observe n events; these can consist of:
nb events from known processes (background)
ns events from a new process (signal)
If ns, nb are Poisson r.v.s with means s, b, then n = ns + nb
is also Poisson, mean = s + b:
Suppose b = 0.5, and we observe nobs = 5. Should we claim
evidence for a new discovery?
Give p-value for hypothesis s = 0:
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Significance from p-value
Often define significance Z as the number of standard deviations
that a Gaussian variable would fluctuate in one direction
to give the same p-value.
TMath::Prob
TMath::NormQuantile
E.g. Z = 5 (a ‘5 sigma effect’) means p = 2.85  10-7
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The significance of a peak
Suppose we measure a value
x for each event and find:
Each bin (observed) is a
Poisson r.v., means are
given by dashed lines.
In the two bins with the peak, 11 entries found with b = 3.2.
The p-value for the s = 0 hypothesis is:
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The significance of a peak (2)
But... did we know where to look for the peak?
→ give P(n ≥ 11) in any 2 adjacent bins
Is the observed width consistent with the expected x resolution?
→ take x window several times the expected resolution
How many bins  distributions have we looked at?
→ look at a thousand of them, you’ll find a 10-3 effect
Did we adjust the cuts to ‘enhance’ the peak?
→ freeze cuts, repeat analysis with new data
How about the bins to the sides of the peak... (too low!)
Should we publish????
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When to publish
HEP folklore: claim discovery when p-value of background only
hypothesis is 2.85  10-7, corresponding to significance Z = 5.
This is very subjective and really should depend on the
prior probability of the phenomenon in question, e.g.,
phenomenon
D0D0 mixing
Higgs
Life on Mars
Astrology
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reasonable p-value for discovery
~0.05
~ 10-7 (?)
~10-10
~10-20
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Wrapping up lecture 3
We looked at statistical tests and related issues:
discriminate between event types (hypotheses),
determine selection efficiency, sample purity, etc.
Some modern (and less modern) methods were mentioned:
Fisher discriminants, neural networks,
support vector machines,...
We also talked about goodness-of-fit tests:
p-value expresses level of agreement between data
and hypothesis
Next we’ll turn to the second main part of statistics:
parameter estimation
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Extra slides
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Probability Density Estimation (PDE) techniques
Construct non-parametric estimators of the pdfs
and use these to construct the likelihood ratio
(n-dimensional histogram is a brute force example of this.)
More clever estimation techniques can get this to work for
(somewhat) higher dimension.
See e.g. K. Cranmer, Kernel Estimation in High Energy Physics, CPC 136 (2001) 198; hep-ex/0011057;
T. Carli and B. Koblitz, A multi-variate discrimination technique based on range-searching,
NIM A 501 (2003) 576; hep-ex/0211019
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Kernel-based PDE (KDE, Parzen window)
Consider d dimensions, N training events, x1, ..., xN,
estimate f (x) with
kernel
bandwidth
(smoothing parameter)
Use e.g. Gaussian kernel:
Need to sum N terms to evaluate function (slow);
faster algorithms only count events in vicinity of x
(k-nearest neighbor, range search).
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Product of one-dimensional pdfs
First rotate to uncorrelated variables, i.e., find matrix A such that
for
we have
Estimate the d-dimensional joint pdf as the product of 1-d pdfs,
(here x decorrelated)
This does not exploit non-linear features of the joint pdf, but
simple and may be a good approximation in practical examples.
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Decision trees
A training sample of signal and background data is repeatedly
split by successive cuts on its input variables.
Order in which variables used based on best separation between
signal and background.
Iterate until stop criterion reached,
based e.g. on purity, minimum
number of events in a node.
Resulting set of cuts is a ‘decision tree’.
Tends to be sensitive to
fluctuations in training sample.
Example by Mini-Boone, B. Roe et
al., NIM A 543 (2005) 577
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Boosted decision trees
Boosting combines a number classifiers into a stronger one;
improves stability with respect to fluctuations in input data.
To use with decision trees, increase the weights of misclassified
events and reconstruct the tree.
Iterate → forest of trees (perhaps > 1000). For the mth tree,
Define a score am based on error rate of mth tree.
Boosted tree = weighted sum of the trees:
Algorithms: AdaBoost (Freund & Schapire), e-boost (Friedman).
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Comparing multivariate methods (TMVA)
Choose the best one!
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Multivariate analysis discussion
For all methods, need to check:
Sensitivitiy to statistically unimportant variables
(best to drop those that don’t provide discrimination);
Level of smoothness in decision boundary (sensitivity
to over-training)
Given the test variable, next step is e.g., select n events and
estimate a cross section of signal:
Now need to estimate systematic error...
If e.g. training (MC) data ≠ Nature, test variable is not optimal,
but not necessarily biased.
But our estimates of background b and efficiencies would then
be biased if based on MC. (True also for ‘simple cuts’.)
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Multivariate analysis discussion (2)
But in a cut-based analysis it may be easier to avoid regions
where untested features of MC are strongly influencing the
decision boundary.
Look at control samples to test joint distributions of inputs.
Try to estimate backgrounds directly from the data (sidebands).
The purpose of the statistical test is often to select objects for
further study and then measure their properties.
Need to avoid input variables that are correlated with the
properties of the selected objects that you want to study.
(Not always easy; correlations may be poorly known.)
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Some multivariate analysis references
Hastie, Tibshirani, Friedman, The Elements of Statistical Learning,
Springer (2001);
Webb, Statistical Pattern Recognition, Wiley (2002);
Kuncheva, Combining Pattern Classifiers, Wiley (2004);
Specifically on neural networks:
L. Lönnblad et al., Comp. Phys. Comm., 70 (1992) 167;
C. Peterson et al., Comp. Phys. Comm., 81 (1994) 185;
C.M. Bishop, Neural Networks for Pattern Recognition, OUP (1995);
John Hertz et al., Introduction to the Theory of Neural Computation,
Addison-Wesley, New York (1991).
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General comments on Bayesian Higgs analysis
The main idea in a Bayesian analysis is to evaluate the probability
of a hypothesis, where here the probability is interpreted
as a (subjective) degree of belief:
The probability of hypothesis H0 relative to its complementary
alternative H1 is often given by the posterior odds:
no Higgs
Higgs
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Bayes factors
The posterior odds is
Bayes factor B01
prior odds
The Bayes factor is regarded as measuring the weight of
evidence of the data in support of H0 over H1.
In its simplest form the Bayes factor is the likelihood ratio.
Interchangeably use B10 = 1/B01
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Bayes factors with undetermined parameters
If H0, H1 (no Higgs, Higgs) are composite, i.e., they
contain one or more undetermined parameters l, then
p(l) = prior, could be based on other measurement or could
be “purely subjective”, e.g., a theoretical uncertainty.
So the Bayes Factor is a ratio of “integrated likelihoods”
(the likeihood ratio uses maximized likelihoods).
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Assessing Bayes factors
One can use the Bayes factor much like a p-value (or Z value).
There is an “established” scale, analogous to our 5s rule:
B10
Evidence against H0
-------------------------------------------1 to 3
Not worth more than a bare mention
3 to 20
Positive
20 to 150
Strong
> 150
Very strong
Kass and Rafferty, Bayes Factors, J. Am Stat. Assoc 90 (1995) 773.
Not clear how useful this scale is for HEP.
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